TF reporter barcode processing - Deep P53/GR scan - stimulation 1

Introduction

18,000 TF reporters on pMT02 were transfected into mESCs, U2OS & A549, sequencing data yielded barcode counts of these experiments. These counts will be processed in this script.

Analysis

Data exploration

Stuff learned from above figures:

1: Most samples have high matched read counts, but some seem to have lower counts:
High unmatched counts:
- A549_Dex-100_rep1/3
Low matched counts:
- MCF7-KO_DMSO_rep1/2
- MCF7-KO_Nutlin_rep2/3 (maybe also 1)
- MCF7-WT_DMSO_rep2/3 - MCF7-WT_Nutlin_rep3 - mES_N2B27-HQ_rep1 (maybe also 2)
- mES_N2B27-RA_rep1/2
-> Remove everything with less than 1700 matched barcodes with more than 500 rpm’s

2: Samples with high counts usually also have a high matched barcode percentage.

3: Barcode counts diverge from pDNA data for almost all samples (except MCF7-WT-DMSO-rep2, and maybe mES_N2B27-RA_rep3) - with this we can exclude that we mainly sequenced barcode from pDNA.

4: Bc counts match with insert-seq -> The GC bias is also present here. I should possibly correct for this.

-> Based on these figures I will exclude all samples that have less than 300 barcodes with at least 500 normalized counts.

Session Info

paste("Run time: ",format(Sys.time()-StartTime))

## [1] "Run time:  3.593597 mins"

getwd()

## [1] "/DATA/usr/m.trauernicht/projects/SuRE_deep_scan_trp53_gr/stimulation_1"

date()

## [1] "Wed Dec  9 14:29:36 2020"

sessionInfo()

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.7 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] grid      parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] ggbiplot_0.55       scales_1.1.0        factoextra_1.0.7   
##  [4] shiny_1.4.0         pheatmap_1.0.12     gridExtra_2.3      
##  [7] RColorBrewer_1.1-2  readr_1.3.1         haven_2.2.0        
## [10] ggbeeswarm_0.6.0    plotly_4.9.2.1      tibble_3.0.1       
## [13] dplyr_0.8.5         vwr_0.3.0           latticeExtra_0.6-29
## [16] lattice_0.20-38     stringdist_0.9.5.5  GGally_1.5.0       
## [19] ggpubr_0.2.5        magrittr_1.5        ggplot2_3.3.0      
## [22] stringr_1.4.0       plyr_1.8.6          data.table_1.12.8  
## 
## loaded via a namespace (and not attached):
##  [1] httr_1.4.1        tidyr_1.0.0       jsonlite_1.7.1    viridisLite_0.3.0
##  [5] splines_3.6.3     prettydoc_0.4.0   assertthat_0.2.1  vipor_0.4.5      
##  [9] yaml_2.2.1        ggrepel_0.8.1     pillar_1.4.3      glue_1.4.2       
## [13] digest_0.6.27     promises_1.1.1    ggsignif_0.6.0    colorspace_1.4-1 
## [17] Matrix_1.2-18     htmltools_0.5.0   httpuv_1.5.4      pkgconfig_2.0.3  
## [21] purrr_0.3.3       xtable_1.8-4      jpeg_0.1-8.1      later_1.1.0.1    
## [25] mgcv_1.8-31       farver_2.0.1      ellipsis_0.3.0    withr_2.1.2      
## [29] lazyeval_0.2.2    crayon_1.3.4      mime_0.9          evaluate_0.14    
## [33] nlme_3.1-143      forcats_0.4.0     beeswarm_0.2.3    tools_3.6.3      
## [37] hms_0.5.3         lifecycle_0.2.0   munsell_0.5.0     compiler_3.6.3   
## [41] rlang_0.4.8       htmlwidgets_1.5.2 crosstalk_1.0.0   labeling_0.3     
## [45] rmarkdown_2.5     gtable_0.3.0      reshape_0.8.8     reshape2_1.4.4   
## [49] R6_2.5.0          knitr_1.30        fastmap_1.0.1     stringi_1.5.3    
## [53] Rcpp_1.0.5        vctrs_0.2.4       png_0.1-7         tidyselect_1.1.0 
## [57] xfun_0.19